From preferences to choices and back again: evidence for human inconsistency and its implications

Christopher Lucas, Carnegie Mellon University

Charles Kemp, Carnegie Mellon University

Thomas Griffiths, University of California, Berkeley

Abstract

People's choices can be predicted given information about their
preferences. Learning people's preferences is the inverse problem of inferring
preferences from choices. Given the apparent relationship between choice
prediction and preference learning, it is natural to ask whether the two are
mutually consistent. Given weak assumptions, we show that no single, consistent
model of the relationship between choices and preferences can explain both the
choices people make and their inferences about others' preferences. This finding
implies that people make systematic errors in learning about others
preferences, and indicates that some accounts of preference learning, e.g., those
based on simulating choices as well as unconstrained rational models, are
inadequate. We also consider alternative assumptions, which allow consistent
models but require a new interpretation of decoy effects in multiattribute
choice.